Intelligent control of a constant turning force system with fixed metal removal rate

Abstract

The maintenance of a constant cutting force operation via control of the turning systems can increase the metal removal rate (MRR) and tool life. However, an increase in cutting depth reduces feed rate during the constant cutting force operation, resulting in lower productivity for the machine tool. To eliminate the problem, this study proposed an MRR scheme to assist a turning system in constructing a constant turning force system with fixed MRR. This study also presented a self-organizing fuzzy controller (SOFC) for manipulating such a system to maintain a constant turning force operation and improve the productivity of the machine tool. Nevertheless, it is difficult to select a suitable learning rate and an appropriate weighting distribution for the design of an SOFC. To overcome the difficulty, this study developed a hybrid self-organizing fuzzy and radial basis-function neural-network controller (HSFRBNC) for such turning systems. The HSFRBNC uses a radial basis function neural-network to adjust in real time the learning rate and the weighting distribution parameters of the SOFC to appropriate values, rather than obtaining the parameters by trial and error. This strategy solves the problem of determining appropriate parameters for designing an SOFC. Simulation results showed that the HSFRBNC achieved better control performance than the SOFC when it came to the control of a constant turning force system with fixed MRR.